Deep listening, slow relationships, world-making: Indigenous and feminist ecological reflections on the Listening Guide and the Live Methods Manifesto
Bibliographic record
Abstract
Rooted in a three-year (2018–2021) research collaboration between Indigenous and white settler researchers in Canada, this article asks: how do we use, adapt and remake methods developed in Euro-western contexts for very different geopolitical contexts, specifically Indigenous ones? How do we do this without minimizing the harms caused by Euro-western methods and epistemologies (some of which are ongoing) while providing ample space for Indigenous histories and approaches to knowledge-making and world-making? We detail how we used and adapted a feminist, relational, narrative data analysis approach – the Listening Guide – in two research projects, reflecting on how and why we made modifications. We expand three key points from the 2012 Live Methods Manifesto, while advancing two arguments about methods in this paper. First, we maintain that as all methods have histories and exist within geopolitical, socio-cultural, philosophical, ethical, epistemological and ontological contexts, they are neither static objects nor recipes that can be uniformly applied. We argue that the Listening Guide, often referred to as a singular approach, is in fact multiple. Second, we assert the ecological liveliness of methods in practice and in their specific contexts – their flow and flux, processes of becoming, relationalities and refusals, disturbances and renewals. This means that the Live Methods Manifesto must also be a living and constantly remade guide that is attentive to diverse research relationships, responsiveness, reflexivity and responsibilities through all phases of research, including pre- and post-research reflections.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.038 | 0.099 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".